A Data-Driven Framework for Unsupervised Monitoring of Transmission Systems Using End-of-Line Testing Data: A Case Study at Ford Motor Company

2026-10-03

AI Summary

This study addresses the limitations of traditional methods in representation capacity, as well as the poor interpretability and high latency of deep learning models, for anomaly detection in high-dimensional nonlinear time series data. We propose a modular, two-stage unsupervised monitoring framework that integrates time series alignment, autoencoder-based nonlinear dimensionality reduction, and statistical control chart techniques. This approach enables Phase I process monitoring with low computational cost, overcomes predefined threshold constraints, and remains accessible to non-technical practitioners. Experimental evaluations on real-world industrial data from Ford Motor Company demonstrate that the proposed method achieves an accuracy of 0.625, a recall of 1.00, and an F1-score of 0.769, significantly outperforming existing baseline models while satisfying the dual requirements of efficiency and interpretability in industrial applications.

Abstract

Sensing technologies have advanced rapidly across industries ranging from energy to automotive manufacturing. These systems generate high-dimensional (HD) data characterized by complex nonlinear patterns and strong temporal dependencies. Traditional statistical monitoring methods are often limited in their ability to capture such nonlinear structure. Likewise, many analytical approaches used in End-of-Line testing rely on predefined thresholds and heuristic rules, which restrict their ability to detect informative anomaly signatures in HD temporal data. In contrast, while modern deep learning and generative AI models offer strong predictive capabilities, they are often unsuitable in applications where data are costly to collect and where the monitoring system must remain interpretable, low-latency, computationally efficient, and usable by non-technical practitioners. To overcome these limitations, we propose an advanced multivariate monitoring framework for HD data. The framework operates in two stages. In the first stage, the data are preprocessed to remove incomplete and non-informative samples and to temporally align time series data. In the second stage, nonlinear dimensionality reduction is performed, followed by anomaly detection through a control chart based phase I monitoring procedure. The framework can be used in both unsupervised and supervised settings, depending on the availability of ground truth labels during training. Moreover, its flexible and modular structure allows practitioners to adapt its components to different domains and operational requirements. We evaluate the proposed framework on real production data from an automotive manufacturing environment at Ford Motor Company. The proposed method achieves higher accuracy, recall, and F1 score than the company's existing model, improving these metrics from 0.50, 0.30, and 0.429 to 0.625, 1.00, and 0.769, respectively.

Problem

• High-dimensional data
• Anomaly detection
• End-of-Line testing
• Unsupervised monitoring
• Transmission systems

Innovation

• unsupervised monitoring
• nonlinear dimensionality reduction
• high-dimensional time series
• anomaly detection
• end-of-line testing